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Record W4387563966 · doi:10.1097/njh.0000000000000991

Improving End-of-Life Care for Nursing Home Residents Using an Interprofessional Approach

2023· article· en· W4387563966 on OpenAlexaff
Steven Burokas, Susan Parker, Cherie Sirard

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsPreparednessNursingEnd-of-life carePsychological interventionMedicinePalliative careHospice careInterprofessional educationHealth care

Abstract

fetched live from OpenAlex

Interprofessional collaboration enhances quality end-of-life care leading to a dignified death. Hospice care uses an interdisciplinary approach to optimize quality of life and mitigate impacts of serious illness. Interventions to improve hospice care delivery have been proven to be effective, but little is known about nursing home staff preparedness, implementation of hospice education, and interprofessional communication. Research is limited on how hospice care can be implemented into the nursing home setting. The purpose of this study was to determine if education combined with a communication tool improved nursing home staff knowledge and improved communication with the hospice team. The descriptive study invited participants to take a preseminar and postseminar survey to assess end-of-life preparedness in terms of willingness, capability, and resilience. A communication tool was implemented to measure collaboration with the hospice team over 3 months. The results from this study suggest education combined with interprofessional communication improves end-of-life care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.149
GPT teacher head0.458
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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